DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14, 17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea, i.e., mental processes and mathematical concepts for collecting instrument data, classifying the data to determine an operating state, and activating or outputting an error procedure based on the classification results, and the claims do not recite additional elements that integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Step 2A, Prong One – Judicial exception (Abstract Idea)
The courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, “methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’” 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). Further, the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
In the instant case, the independent claim 1 recite limitations that, when considered in their broadest reasonable interpretation, fall within the abstract idea of (i) mental process (concepts formed in the human mind such as observation, evaluation, and judgment) and/or (ii) mathematical concepts (relationships, comparisons, and classifications). For instance, the independent claim 1 recites (independent claim 19 and 20 each recites same limitations):
applying a trained classifier to instrument data, obtained from the analytical instrument, during operation of the analytical instrument,
to detect in which operating state of a plurality of operating states the sample introduction system is operating, wherein the plurality of operating states includes a normal state and a failure state; and
activating an error procedure in response to detecting that the sample introduction system is operating in a failure state;
wherein the instrument data comprises signal data obtained from an analytical measurement made by the analytical instrument; and
wherein the trained classifier is trained using a training data set comprising instrument data corresponding to the normal state of the sample introduction system
These limitations collectively recite collecting information, evaluating and classifying the information according to known normal/failure patterns, and acting based on classification result. Such observation, evaluation, comparison, classification, and judgment of data are mental process that could be performed by a human, for example, by comparing observed instrument data to known normal/failure reference data or a diagnostic table. The claims also recite mathematical concepts. The “trained classifier” performs mathematical classification of input data into operating-state categories, which are merely mathematical transformations used to implement the abstract data analysis.
Step 2A, Prong Two – Integration into a Practical Application
The claims are not integrated into a practical application because in practice, executing all of the steps is indistinguishable from: (i) mere data acquisition from a conventional analytical instrument environment, and (ii) generic computer implementation of the abstract classification analysis. That is to say that integration into a practical application is lacking where, as here, the abstract idea has no effect on the material world or the execution of the process.
Although the claims are limited to a sample introduction system of an ICP analytical instrument and recite analytical signal data/sensor data, these limitations function as data gathering limitations. The data is merely used as input to the abstract classification process. The claims do not change how the instrument physically generates the signal, how the sample introduction system physically operates, or how the sensors function. Rather, the claims recite post-acquisition analysis and interpretation of acquired data using a generic classifier.
Therefore, the claims as a whole are directed to an abstract idea.
Step 2B– Significant More (Inventive Concept)
The claims do not include additional elements, either individually or as an ordered combination, that amount to significant more than the abstract idea. The recited analytical instrument/sample introduction system provides a conventional environment for acquiring data. The recited instrument data, signal data, sensor data, and training data are conventional data inputs used by a classifier. The recited classifier is generic mathematical/computer-implemented tools for classifying data.
The additional elements included in the dependent claims also do not amount to significant more than the abstract idea. Claims 2-6 merely define additional operating state labels or failure categories. Claims 7-11 and 13-14 merely define particular types of input/training data. Claim 12 merely defines generic mathematic/machine-leaning implementation details. Claim 17’s notification limitation merely reports the result of the classification, and the safe mode feature is recited broadly and alternately, without requiring a particular technological improvement to the instrument. Thus, the additional limitations do not apply the abstract idea in a meaningful way beyond generally linking the classification to an analytical instrument environment.
Taken alone or as ordered combination, claims 1-14, 17, and 19-20 fail to recite patent eligible subject matter.
Claim Objections
Claims 6 and 17 are objected to because of the following informalities:
Claim 6 recites “a nebulizer flow that deviates from what an expected nebulizer flow” which is grammatically incomplete since it is unclear what the nebulize flow deviates from.
Claim 17 recites “placing at least one of the sample introduction system the analytical instrument into a safe mode”, appears to omit a connector such as “and,” “or” or “and/or” between “the sample introduction system” and “analytical instrument”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7, 11-13, 17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2020/0387790 A1 [hereinafter Carneiro].
Regarding Claim 1:
Although claim 1’s preamble recites an ICP analytical instrument, the claimed steps do not require any ICP-specific operation. Claim 1 does not require the classifier to use any ICP-specific plasma property or to control any ICP-specific component. Therefore, for claim 1, the “ICP analytical instrument” element is interpreted broadly as identifying the analytical instrument context, while the operative limitations are the trained-classifier failure detection steps applied to instrument data.
Carneiro teaches a method of operating a sample introduction system of an inductively coupled plasma analytical instrument (paras. [0011,0021]: “methods…for monitoring the performance of analytical instruments,” and the analytical instrument may include “a mass analyzer system…a mass spectrometer (MS system) …a sample introduction system”), the method comprising:
applying a trained classifier to instrument data, obtained from the analytical instrument, during operation of the analytical instrument (paras. [0012, 0014, 0022, 0025, 0034]: the analytical instrument (e.g., devices 115a-n) “may operate to perform an analysis and generate analytical information 144”, which is provided to a computing device 110. The diagnostic services logic 122 included in the computing device “may operate to generate models associated with devices 115a-n”)
to detect in which operating state of a plurality of operating states the sample introduction system is operating, wherein the plurality of operating states includes a normal state and a failure state (paras. [0011, 0033]: “diagnostic services logic 122 may be configured to provide and/or implement diagnostic services for devices 115a-n…for determining whether devices 115a-n and/or components thereof are operating normally or abnormally); and
activating an error procedure in response to detecting that the sample introduction system is operating in a failure state (paras. [0014, 0058]: provide the “diagnosis for an instrument, system, device, component…to an operator for review, analysis, annotation (or “mark-up”), and/or the like.” For example, a training supervisor may tag the diagnosis column as “BAD” when diagnosis results showing a component of the system operating abnormally);
wherein the instrument data comprises signal data obtained from an analytical measurement made by the analytical instrument (para. [0023]: the analytical information generated by an analytical instrument as a result of performing analysis, including “raw…unprocessed data, chromatograms, spectra, peak lists, mass values, retention time values, concentration values, compound identification information,” etc.); and
wherein the trained classifier is trained using a training data set comprising instrument data corresponding to the normal state of the sample introduction system (para. [0016]: “computational models may be generated for analytical systems… may include standard models and/or model data associated with standard operations (for instance, normal, expected, proper, within-range or threshold, and/or the like)”).
Regarding Claim 2:
Carneiro teaches the method of claim 1. Carneiro further teaches wherein the failure state includes a plurality of failure sub-states each corresponding to one of a plurality of failure categories (para. [0033]: the diagnosing sources of abnormal operating includes “a failing pump, an inadequate seal, degraded reagent,” etc.).
Regarding Claim 3:
Carneiro teaches the method of claim 1. Carneiro further teaches wherein the plurality of operating states further includes a close-to-failure state (para. [0033]: the operating states include “an imminent failure condition”).
Regarding Claim 4:
Carneiro teaches the method of claim 3. Carneiro further teaches wherein the close-to-failure state includes a plurality of close-to-failure sub-states each corresponding to one of a plurality of failure categories (paras. [0033, 0038]: detecting imminent failure at different diagnostic levels, including device/component/function/property levels, and examples include pump, valve, column, temperature, pressure voltage, and status).
Regarding Claim 5:
Carneiro teaches the method of claim 3. Carneiro further teaches wherein the plurality of failure categories comprises at least one of:
a leaking component of the sample introduction system (para. [0033]: diagnosing a source of abnormal operation, for example, “an inadequate seal”);
a clogged component of the sample introduction system (para. [0024]: “blockage detection”);
a damaged component of the sample introduction system (para. [0033]: diagnosing a source of abnormal operation, for example, “a failing pump”); and
a flow through a component of the sample introduction system that deviates from an expected flow (para. [0054]: “verify flow rate is ideal”).
Regarding Claim 7:
Carneiro teaches the method of claim 1. Carneiro further teaches wherein the sample introduction system comprises one or more sensors, and wherein the instrument data further comprises sensor data comprising outputs from the one or more sensors (para. [0022]: operating information obtained through data channels associated with devices/components/sensor, including “pressure sensors, temperature sensors, flow sensors,” etc.).
Regarding Claim 11:
Carneiro teaches the method of claim 1. Carneiro further teaches wherein the signal data comprises spectrometric data (para. [0023]: analytical information including “spectra, peak lists, mass values, retention time values, concentration values, compound identification information, and/or the like”).
Regarding Claim 12:
Carneiro teaches the method of claim 1. Carneiro further teaches wherein the trained classifier comprises a trained machine learning algorithm, wherein optionally the trained machine learning algorithm comprises a neural network (para. [0034]: “the models may include various computational models, including … neural network models”).
Regarding Claim 13:
Carneiro teaches the method of claim 1. Carneiro further teaches wherein the training data set further comprises instrument data corresponding to the failure state of the sample introduction system (para. [0016]: “The computational models may include non-standard models and/or non-standard model data associated with non-standard operations in which the systems and/or components thereof (for instance, analytical instrument components, and/or associated devices) are operating improperly, outside of accepted limits, and/or the like”).
Regarding Claim 17:
Under the broadest reasonable interpretation, the phase “at least one of X, Y, and Z” encompasses X alone, Y alone, Z alone, or any combination thereof. Therefore, a reference teaching any one of the limited alternatives satisfies the limitation. For claim 17, Carneiro at least teaches one of the alternatives “notifying the failure state to a user” therefore reads on the claim.
Carneiro teaches the method of claim 1. Carneiro further teaches wherein the error procedure comprises at least one of: notifying the failure state to a user (para. [0053]: “The processed diagnosis for the entire instrument/system and each device on that instrument/system may be reported to a data consumer, such as an operator, …a training supervisor”); and placing at least one of the sample introduction system the analytical instrument into a safe mode, wherein optionally the safe mode comprises any of: stopping the sample introduction system; stopping one or more components of the sample introduction system; and preventing a sample from entering a nebulizer of the sample introduction system.
Regarding Claim 19:
Carneiro teaches an apparatus arranged to carry out a method according to claim 1 (para. [0011]: “apparatus for monitoring the performance of analytical instruments”).
Regarding Claim 20:
Carneiro teaches a computer-readable medium storing a computer program which, when executed by a processor, causes the processor to carry out a method according to claim 1 (para. [0070]: “The drives and associated computer–readable media provide volatile and/or nonvolatile storage of data, data structures, computer-executable instructions”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Carneiro in view of JP 2004127709A [hereinafter Hitachi].
Regarding Claim 6:
Under the broadest reasonable interpretation, the phase “at least one of X, Y, and Z” encompasses X alone, Y alone, Z alone, or any combination thereof. Therefore, a reference teaching any one of the limited alternatives satisfies the limitation. For claim 6, Hitachi at least teaches one of the alternatives “a clogged nebulizer”, therefore reads on the claim.
Carneiro teaches the method of claim 5. However, Carneiro does not specifically note that wherein the plurality of failure sub-states comprises at least one of: a leaking sample tube; a clogged nebulizer; a nebulizer flow that deviates from what an expected nebulizer flow; a leaking peristaltic pump tube; a damaged peristaltic pump tube; and an empty sample vial.
Hitachi teaches wherein the plurality of failure sub-states comprises a clogged nebulizer (paras. [0019]: “In a plasma ion source mass spectrometer having a nebulizer for spraying a liquid sample in a mist and identifying and quantifying trace components in the liquid sample, … the flow rate detected by the flow rate sensor are determined to be within a predetermined range that is less than the flow rate when the flow path is not clogged”).
Carneiro teaches determining operating state of an analytical instrument and its components using analytical measurement data obtained from analytical instrument, including MS system, sample preparation system, sample introduction system, etc. Hitachi teaches detecting clogging of a nebulizer in an ICP by detecting a liquid flow rate through the flow path. Therefore, it would have been obvious for an ordinary skilled person in the art, before the effective time of filing, to modify Carneiro’s diagnostic system to identify the ICP sample introduction failure of a clogged sample-supply flow path, as taught in Hitachi. One of ordinary skilled would be motivated to include such a known clogged low path condition of a nebulizer as a failure sub-state because clogging of the sample introduction path directly affects sample delivery to the analytical instrument and can cause unreliable analysis results.
Regarding Claim 14:
Under the broadest reasonable interpretation, the phase “at least one of X, Y, and Z” encompasses X alone, Y alone, Z alone, or any combination thereof. Therefore, a reference teaching any one of the limited alternatives satisfies the limitation. For claim 14, Hitachi at least teaches one of the alternatives “a nebulizer flow sensor”, therefore reads on the claim.
Carneiro teaches the method of claim 7. However, Carneiro does not specifically note that wherein the sensor data comprises data obtained from at least one of: a nebulizer backpressure sensor; a nebulizer flow sensor; a cooling gas flow sensor; a radio frequency plasma power sensor; and a peristaltic pump speed sensor.
Hitachi teaches wherein the sensor data comprises data obtained from a nebulizer flow sensor (para. [0019]: detecting flow rate in the nebulizer flow path using flow rate sensor).
Carneiro teaches using operating information from instrument sensors, including flow/pressure/component operating data, to diagnose analytical instrument operating condition. Hitachi teaches detecting clogging of a nebulizer in an ICP by detecting a liquid flow rate through the flow path via a flow rate sensor. Therefore, it would have been obvious for an ordinary skilled person in the art, before the effective time of filing, to use the flow rate sensor to collect signal data, as taught by Hitachi, as sensor data input to Carneiro’s diagnostic system. One of ordinary skilled would be motivated to use such sample introduction flow sensor to collect data because the sensor data reflects whether sample is being properly supplied to the nebulizer and would improve detection of sample instruction failures during instrument operation.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Carneiro in view of Dennaud et al., (2001). Study of ionic-to-atomic line intensity ratios for two axial viewing-based inductively coupled plasma atomic emission spectrometers. Spectrochimica Acta Part B Atomic Spectroscopy, 56(1), 101–112 [hereinafter Dennaud].
Regarding Claim 8:
Carneiro teaches the method of claim 1. However, Carneiro does not specifically note that wherein the signal data comprises data that is representative of a property of the inductively coupled plasma, wherein optionally the data that is representative of a property of the inductively coupled plasma comprises an amount of a first species present in the plasma.
Dennaud teaches wherein the signal data comprises data that is representative of a property of the inductively coupled plasma, wherein optionally the data that is representative of a property of the inductively coupled plasma comprises an amount of a first species present in the plasma (Page 2: “The use of the Mg II/ Mg I ratio…proved to be a simple but…efficient way to follow the plasma conditions… The ideal case is observed when both no change in the Mg II /Mg I ratio and analyte signal are observed… but the variation in the analyte signal can be explained by problems that arise at the aerosol transport and filtration level, i.e. via the spray chamber”).
Carneiro teaches determining operating state of an analytical instrument and its components using analytical measurement data obtained from analytical instrument, including MS system, sample preparation system, sample introduction system, etc. Dennaud teaches that plasma emission signal data, such as ionic-atomic line intensity information, is a known indicator of ICP plasma condition, i.e., whether the instrument operating normally or encountered problems. Therefore, it would have been obvious for an ordinary skilled person in the art, before the effective time of filing, to use Dennaud’s ICP plasma property signal data as the analytical signal input in Carneiro’s diagnostic system, to monitor changes in plasma condition during ICP operation and improve collection of abnormal sample introduction operating using data already generated by the analytical measurement.
Regarding Claim 9:
Because the claim recites the wherein feature as “optionally”, the feature is not a required limitation of the claim.
Carneiro in view of Dennaud teaches the method of claim 8. Dennaud further teaches wherein the signal data comprises data that is a ratio of the amount of the first species present in the plasma and a second species present in the plasma (Abstract: teaches comparing observed multiple ionic-to-atomic line intensity ratios, including Mg II/Mg I, Ni II/Ni I, Zn II/Zn, where each ratio reflects plasma conditions in ICP), wherein optionally the signal data comprises a ratio of the amount of Argon in the plasma and the amount of nitrogen in the plasma.
Regarding Claim 10:
Carneiro in view of Dennaud teaches the method of claim 8. Dennaud further teaches wherein data that is representative of an amount of a species in the plasma comprises a recorded intensity of the species emissions in the plasma (a “line intensity” is a recorded emission intensity of a species in the plasma).
Claims 15-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Carneiro in view of WO 2021142532A1 [hereinafter Burns].
Regarding Claim 15:
Carneiro teaches the method of claim 1. However, Carneiro does not specifically note that wherein a first activation function of the trained classifier comprises a rectified linear function and wherein optionally a second activation function of the trained classifier comprises a softmax function. Burns teaches wherein a first activation function of the trained classifier comprises a rectified linear function and wherein optionally a second activation function of the trained classifier comprises a softmax function (P.22: 10-11& 20-21: an FCN classifier whose core “consists of 1D convolutional layers, with rectified linear unit (ReLU) activation… with a final dense layer with softmax activation”).
Carneiro teaches using neural network diagnostic models to classify and diagnose normal and abnormal operating condition of analytical instrument. Burns teaches a conventional neural network classifier architecture using ReLU hidden layer activation and softmax output activation optimization for classification. Therefore, it would have been obvious for an ordinary skilled person in the art, before the effective time of filing, to implement Carneiro’s neural network diagnostic classifier using ReLU hidden layer activation and softmax output activation for classification, as taught by Burns, because this is known standard tools for training a multi-class classifier to output class probabilities and minimize classification error.
Regarding Claim 16:
Carneiro teaches the method of claim 1. However, Carneiro does not specifically note that wherein a loss function of the trained classifier comprises a categorical cross entropy function. Burns teaches wherein a loss function of the trained classifier comprises a categorical cross entropy function (P.22: 21-22 “The FCN classifier was trained for 150 epochs using the adam optimizer, categorical cross entropy loss”).
Carneiro teaches using neural network diagnostic models to classify and diagnose normal and abnormal operating condition of analytical instrument. Burns teaches a conventional neural network classifier architecture using categorical cross-entropy loss function for classification. Therefore, it would have been obvious for an ordinary skilled person in the art, before the effective time of filing, to implement Carneiro’s neural network diagnostic classifier using categorical cross-entropy loss function for classification, as taught by Burns, because this is known standard tools for training a multi-class classifier to output class probabilities and minimize classification error.
Regarding Claim 18:
Carneiro teaches the method of claim 1. However, Carneiro does not specifically note that the method further comprising generating the trained classifier by performing Adam optimization on an initial classifier using the training data set. Burns teaches the method further comprising generating the trained classifier by performing Adam optimization on an initial classifier using the training data set (P.22: 21-22 “The FCN classifier was trained for 150 epochs using the adam optimizer, categorical cross entropy loss”).
Carneiro teaches using neural network diagnostic models to classify and diagnose normal and abnormal operating condition of analytical instrument. Burns teaches a conventional neural network classifier architecture using Adam optimization for classification. Therefore, it would have been obvious for an ordinary skilled person in the art, before the effective time of filing, to implement Carneiro’s neural network diagnostic classifier using Adam optimization for classification, as taught by Burns, because this is known standard tools for training a multi-class classifier to output class probabilities and minimize classification error.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JING WANG whose telephone number is (571)272-2504. The examiner can normally be reached M-F 7:30-17:00.
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/JING WANG/Examiner, Art Unit 2881
/WYATT A STOFFA/Primary Examiner, Art Unit 2881
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